A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults

Abstract

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain’s core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults ( n = 478, ages 40 to 90 y). First, we decomposed each subject’s functional brain network using k -shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.

Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks

“…For subjects older than 65, we used a cutoff of 23/30, which has been found to limit false diagnosis of mild cognitive impairment [80]. To reduce the likelihood of inclusion of participants with forms of dementia the MoCa may be less sensitive to (e.g., vascular, semantic or frontotemporal dementia), we also excluded subjects between the ages of 65-90 with poor performance on measures of cognitive flexibility, vocabulary comprehension, and executive function [17]. Poor performance was defined as a performance level worse than two standard deviations below the mean.…”

“…We used participant's MoCA scores to determine if they were healthy (score >= 23/30). Additionally, we used measures of cognitive flexibility, assessed via the used the Dimensional Card Sort Test [86], executive control, assessed via the Flanker Inhibitory Control and Attention Test [87], and vocabulary comprehension, assessed via the Picture Vocabulary Test [88], to exclude subjects with forms of dementia that the MoCA is insensitive to [17].…”

“…We used the Bonferonni method to correct multiple comparisons. In each panel we corrected for the number of networks analyzed (17). In panels (B) and (D), the mediation was significant if the confidence intervals did not cross 0 when the α = 0.05/17 to correct for multiple comparisons.…”

“…Studying the topological properties of macroscopic brain connectivity with tools from network science [8] is one method by which the mechanisms that could promote cognitive function in aging were examined. Studies focused on measures of network topology that change throughout healthy [9][10][11][12][13][14][15][16][17], and pathological aging [18][19][20][21][22][23][24], and attempted to relate alterations in topology to cognition. One such central measure is network controllability [25].…”

“…For GM, we used IC-volumeadjusted measures of hippocampal volume, subcortical volume, and cortical volume.F) The z-scored predicted processing speed versus real z-scored processing speed for the best model shown in (E). In panels A, and B, we used the Bonferroni method to correct for multiple comparisons based on the number of networks analyzed(17). For panels D-E, measures of processing speed and GM volume were z-scored.…”